Re: st: Multivariate kernel regression

Hi Austin (and others),
Thank you very much for your reply. Sorry about my delayed response -
I wanted to investigate more to make sure I understood your
suggestion.
I'm not sure your suggestion gets me exactly what I was looking for,
and I want to clarify. My reference to -lpoly- in my initial post may
have been confusing. I don't actually want to do kernel-weighted local
regressions. I want to estimate "multivariate kernel regression",
which to my understanding, doesn't actually involve any regressions at
all. It takes the weighted average of Y for all observations near to
the particular value of X, weighted using the kernel function. And
where X represents more than 2 variables. So, this actually seems the
same to me as multivariate kernel density estimation, which I also
don't see any user-written commands for in Stata. What I am looking
for, I guess is like a version of -kdens2- that allows for more than
one "xvar", and wouldn't output a graph (since it would be in greater
than 3 dimensions), but rather would output the fitted or predicted
values of the Y (like -predict, xb-) for each observation.
Regardless, it sounds like given your suggestion, one way to do this
is to loop over all possible combinations of the values of the X
variables and calculate the weighted Y for each combination using the
kernel of my choice? Please let me know if this would be your
suggestion, or if given my further clarification, if you know of any
user-written commands in Stata to do this, or if you have any other
suggestions.
Thanks a lot for your help, and sorry again for the delayed response.
Josh
On Fri, Oct 12, 2012 at 3:31 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Josh Hyman <hyman.josh@gmail.com>:
> If you know the multivariate kernel you want to use, and the grid you
> want to smooth over, it is straightforward to loop over the grid and
> compute the regressions. To program a general estimator for a wide
> class of kernels would be substantially more work. See e.g. -kdens-
> on SSC and
> http://fmwww.bc.edu/repec/bocode/m/mf_mm_kern
> http://fmwww.bc.edu/RePEc/bocode/k/kdens.pdf
>
> A simple conic (triangle) kernel in 2 dimensions is easiest, see e.g.
> http://fmwww.bc.edu/repec/bocode/t/tddens
>
> On Fri, Oct 12, 2012 at 1:49 PM, Josh Hyman <hyman.josh@gmail.com> wrote:
>> Dear Statalist users,
>>
>> I am trying to figure out if there is a way in Stata to perform
>> multivariate kernel regression. I have investigated online and on the
>> Statalist, but with no success. What I am looking for would be similar
>> conceptually to the -lpoly- command, but with the ability to enter more
>> than one "xvar".
>>
>> If there are no Stata commands to do this (user-written or otherwise), then
>> do you recommend coding up a program to do this manually? I have used Stata
>> for many years, and written programs before, but have never had to code up
>> a regression manually. If you have suggestions on how to do this, or
>> resources to consult, that would be greatly appreciated.
>>
>> Please let me know if I can provide any other information. Thank you for
>> your consideration,
>> Josh Hyman
>> Economics doctoral candidate
>> University of Michigan
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